Basically, our proposed network is modified based on the U-Net [1]} architecture, which features a U-shape topology and the skip connections between the encoder and the decoder. In the proposed Prior Attention Network, a novel attention guiding decoder module is integrated to the skip connections of the network to refine the feature representations by spatial attention. A novel parameterized skip connection is also introduced to the network to guide the network to learn the ratio between vanilla feature maps and refined feature maps. The attention guiding decoder takes the semantic-rich features from the encoder and generate spatial attention maps to guide the following multi-class segmentation. To generate ROI-related attention, the intermediate supervision strategy has been utilized in the framework. The refined feature maps are then sent to the multi-class decoder for final dense predictions. The deep supervision strategy is utilized in the multi-class decoder to acquire better convergence and improve segmentation performance. Such network topology achieves the two steps of conventional cascaded networks in a single network through the attention maps generated by the attention guiding decoder. The network scheme is shown in Fig. REF .
